The United States will reach one million unmanned aircraft systems (UAS) flights per day within the next 20 years, given the right regulatory environment, according to new economic research from the Consumer Electronics Association.
Brian Markwalter, senior vice president, market research and standards, CEA, shared the association’s domestic UAS economic analysis at the Unmanned Systems 2015 Conference in Atlanta, Ga.
“This is a billion-dollar technology market literally just waiting to take off,” Markwalter said. “We see a dynamic market with tremendous growth potential, once we have final Federal Aviation Administration (FAA) rules to allow commercial UAS operation, combined with continued industry and FAA cooperation to achieve low-risk, beyond-line-of-sight flights.”
“With the right regulatory environment, drones will be safely integrated into our transportation system — displacing noisy trucks, reducing urban traffic, cutting our fuel consumption and carbon emissions,” said Gary Shapiro, president and CEO, CEA. “This will allow for game-changing innovations such as the quick delivery of life-saving diagnostics and medicine, improvements in crop production and efficiency, and safer work environments for those who inspect and maintain our buildings and bridges.”
According to the CEA research, the U.S. UAS market is indeed growing, but risks falling behind in the global market because of fewer or more progressive regulations in other countries. In fact, as the U.S. awaits further FAA rules regarding the commercial use of UAS, CEA’s research estimates a pent-up market demand of $150-$200 million in UAS sales for “line of sight” operations.
Only hobbyists and the do-it-yourself community now are allowed to fly UAS in the U.S., enough to fuel a robust U.S. consumer market with the potential to reach $250 million by 2018. However, if the FAA remains on track to complete its line-of-sight rules for commercial operators within three years, CEA’s research foresees another $200 million in growth. Additionally, with the continued development of “sense and avoid” technology and FAA rules that foster “beyond-line-of-sight” operations, the United States’ UAS industry could become a $1 billion market.
“The ability for beyond-line-of-sight is the true game changer—opening the door to autonomous UAS operation and unleashing a remarkable economic potential,” said Markwalter. “The United States has a long history of being a technology leader—and we’ve led the world at almost every stage of flight innovation. But we have more work to do on UAS. Realizing these economic gains will require ongoing FAA and industry cooperation, as well as a commitment to the necessary infrastructure.”
CEA market research expects 2015 to be a defining year for unmanned systems, with the category ideally positioned for steady growth. According to CEA projections, the global market for consumer UAS will approach $130 million in revenue in 2015, increasing by more than 50 percent from 2014; with unit sales of consumer UAS expected to approach 425,000, an increase of 65 percent.
“Right now, more than six billion packages are delivered every year in the U.S., weighing less than three pounds apiece on average — perfect candidates for drone delivery,” said Markwalter. “The autonomous operation of UAS for the delivery of everyday items would not only lower the cost for consumers and improve delivery times, but also be a significant driver of our tech economy.”
This year CEA debuted the Unmanned Systems Marketplace at the 2015 International CES, with 15 UAS companies — almost four times as many as last year — covering 7,600 square feet of exhibit space. At CES, Shapiro announced CEA’s support of the UAS safety campaign “Know Before You Fly,” which provides prospective UAS operators with the information and guidance they need to fly safely and responsibly.
Trimble has announced a series of new software enhancements that enable photogrammetry, GIS, geospatial and remote sensing professionals to streamline workflows, achieve faster results and gain increased value from highly accurate geospatial data. Enhancements include the Trimble Inpho version 6.1 photogrammetric suite, UASMaster version 6.1 and UASMaster Lite for Unmanned Aircraft System (UAS) applications, and eCognition version 9.1 and eCognition Essentials version 1.1 image analysis software.
The announcement was made at the Imaging and Geospatial Technology Forum (IGTF), formerly ASPRS, held May 4-8 in Tampa, Fla.
“Optimizing software workflows for our customers to gain value from imaging data is critical for the success of geospatial professionals and a continued focus of Trimble Geospatial,” said Alain Samaha, business area director of GIS and Geospatial Software Solutions for Trimble’s Geospatial Division. “The new enhancements will enable customers to streamline processes and increase their efficiency and productivity, which translates to increased cost savings and decreased operational expenditures.”
Photogrammetry professionals generating high-quality deliverables, with Trimble’s Inpho software, such as 3D CAD line work, GIS layers and DTMs, can now reduce production time by days through optimized geo-referencing capabilities and new tools for CAD object creation. The Inpho version 6.1 enhancement allows snapping-to-elevation and draping lines-to-elevation models—for greater efficiency in creating CAD data layers—while maintaining the highest level of accuracy.
The UASMaster version 6.1 software enhancement offers greater productivity through new support for precise GNSS data that allows users to reduce the number of ground control points required without compromising accuracy. For professionals new to the UAS market, UASMaster is now also available in an entry-level “Lite” edition. The new UASMaster Lite edition allows users to quickly extract high quality deliverables within a simplified workflow, while obtaining the same industry-leading quality offered with Inpho software.
Inpho version 6.1 and UASMaster version 6.1 now also include a direct interface connection to Trimble’s eCognition analysis software, making it easier to obtain actionable and valuable information from imagery data in land classification maps, GIS layers and change analysis.
eCognition version 9.1, an object-based image analysis software, now includes enhanced multi-core processing, allowing GIS, geospatial and remote sensing professionals to extract valuable information from satellite and aerial based data faster than before. New GIS-based analytic tools and improved tools for packaging applications make it easier to create customer solutions.
eCognition Essentials version 1.1 provides up to 50-percent faster processing than previously, including improved flexibility and control of classification workflows for professionals generating land-cover mapping deliverables.
The GPS World staff is reporting live from Unmanned Systems 2015, held May 4-7 in Atlanta. The event convenes the global community of commercial and defense leaders in intelligent robotics, drones and unmanned systems, hosted by AUVSI.
Check back throughout the week for event updates, including news, photos, videos, tweets and more.
The Association for Unmanned Vehicle Systems International’s (AUVSI’s) Unmanned Systems 2015 show, held May 4-7 in Atlanta, convenes the global community of commercial and defense leaders in intelligent robotics, drones and unmanned systems.
The Geospatial Solutions staff is reporting live from Unmanned Systems 2015, held May 4-7 in Atlanta. The event convenes the global community of commercial and defense leaders in intelligent robotics, drones and unmanned systems, hosted by AUVSI.
Check back throughout the week for event updates, including news, photos, videos, tweets and more.
Integrated Navigation Systems Using Parallel Filtering
The authors look at the development of a robust navigation system employing a GNSS receiver, accelerometers, gyroscopes, magnetometers, an airspeed device and dead reckoning to supply a blended navigation solution to a flight control system on a small, unmanned aerial vehicle.
By Trevor Layh and Demoz Gebre-Egziabher
INNOVATION INSIGHTS by Richard Langley
THE NUMBER FOUR has special significance to humankind. According to Penelope Merritt (a Samuel Beckett scholar) “[f]our has long been a number of completion, stability and predictability, as well as the representation of all earthly things.” And so it is with navigation systems. There are four important requirements of any navigation system: accuracy, availability, continuity, and integrity. To quickly review:
Accuracy describes how well a measured value agrees with a reference value, typically the true value.
Availability refers to a navigation system’s ability to provide the required function and performance within the specified coverage area at the start of an intended operation.
Continuity is the ability of a navigation system to function without interruption during an intended period of operation.
Integrity refers to the trustworthiness of a navigation system. A system might be available at the start of an operation, and we might predict its continuity at an advertised accuracy during the operation. But what if something unexpectedly goes wrong? If some system anomaly results in unacceptable navigation accuracy, the system should detect this and declare that it can no longer be used for navigation at the expected accuracy level. GPS, for example, has built into it various checks and balances to ensure a fairly high level of integrity. The same may be said of other global navigation satellite systems. Satellite performance is continuously monitored and a satellite is set unhealthy when an anomaly is detected. Some receivers have built-in receiver autonomous integrity monitoring to detect and isolate problematic satellite signals and navigation support systems (such as the Wide Area Augmentation System) independently monitor the health of satellite signals and supply a timely warning in the case of anomalous signal behavior.
However, an aircraft, vessel, vehicle or some other platform still needs to be able to navigate if an independent primary navigation system becomes unavailable. This requires a back-up system of some kind and may take the form of an inertial navigation system, another radionavigation system such as eLoran, celestial navigation or just dead reckoning. Ideally, the platform’s navigation system should have multiple integrated sensors so that it continues to operate seamlessly even in the event of a sensor failure. We would call such a system robust. While we often use this word to describe a person with a strong healthy constitution, we can apply it to systems to refer to their ability to tolerate perturbations that might affect their functionality. A robust navigation system employs multiple sensors and uses appropriate filtering systems to autonomously detect anomalies, such as a failed sensor, and then to isolate it from the combined navigation solution.
It is important to catch navigation sensor failures early, ideally instantaneously, to reduce integrity risk as much as possible. This is not a trivial operation, and it requires clever software design and operation.
In this month’s column, we look at the development of such a robust navigation system employing a GNSS receiver, accelerometers, gyroscopes, magnetometers, an airspeed device and dead reckoning to supply a blended navigation solution to a flight control system on a small, unmanned aerial vehicle.
While the number four has special significance in religion, science and other aspects of our lives, the number five may be considered equally important — denoting, for example, how many digits we have on our hands and feet. For those mathematically inclined, it is the first safe prime number. And perhaps we should use it to more fully characterize a navigation system, denoting its accuracy, availability, continuity, integrity and robustness.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. Email him at lang @ unb.ca.
Multi-sensor navigation systems generate an estimate of a vehicle’s state vector by fusing information from a disparate set of sensors. In many instances the sensors used in these systems provide redundant information. For example, in GNSS receivers, more than four (the minimum number required) satellite measurements are used to generate a position, navigation and time or PNT solution. This redundancy is beneficial because it enhances accuracy. It also enhances integrity or robustness because it allows the detection and possibly the isolation of failed sensors. However, fault detection and isolation schemes do not work instantaneously because once a sensor has failed, it takes some time before this can be detected. This is especially true for failures that are drift-like in nature as opposed to step-like. Drift-like errors grow slowly and, thus, fault detection schemes that monitor filter residuals cannot detect them until they have grown to a point where they are sufficiently large to exceed preset thresholds.
The time between the onset of a fault and its detection — called the detection time — depends on the fault magnitude and thresholds of the fault detection algorithms. For a given fault magnitude, the length of the detection time represents a compromise between a navigation system’s continuity performance (or false alarm rate) and integrity risk (missed detection probability). The fact that faults cannot be detected instantaneously is an issue particularly for systems that have some form of dead reckoning (such as inertial navigation or velocity-based odometry) integrated with aiding sensors such as GNSS or radars. A failure in the aiding system (for example, a pseudorange fault in GPS) will lead to a corruption of the dead-reckoning solution. Once the GNSS fault has been detected and subsequently removed, the error induced by this failure has already propagated into the dead-reckoning solution. How does one deal with these types of errors? In this article, we discuss a solution to this challenge, which we call parallel filtering.
Solutions for dealing with the problem exist. For example, one approach that has been used is based on the idea of delayed measurements. In this approach, integration of aiding sensor measurements in the navigation solution is delayed until a period equal to the fault detection time has elapsed. If no faults are detected during this period, then the delayed measurements are extrapolated forward in time and integrated into the navigation solution. Alternately, we can rewind the dead-reckoning solution backwards in time, integrate the delayed measurements and fast-forward the integrated solution up to the current time epoch. While this approach works, it has several shortcomings, of which we will mention just two. First, it requires buffering sensor data. Second, the most current navigation solution is not as accurate as it can be, because it does not incorporate the most recent sensor measurements (that is, the delayed measurements). The parallel filtering approach and fault tolerance we describe in this article deals with both of these shortcomings. Of course, like any other engineering solution, it represents a compromise between competing requirements. We will discuss these compromises and their impacts later in the article. For now, we will concentrate on describing the mechanics of parallel filtering and its performance when implemented in an integrated flight control system used for navigation, guidance and control of small unmanned aerial vehicles or UAVs.
Parallel Filtering
To understand parallel filtering, consider the schematic in FIGURE 1, which represents the conventional way in which an integrated navigation system fuses the information from N sensors. All the measurements from the N sensors are integrated in a single sensor-fusion algorithm. In the context of what we are describing here, the algorithm consists of a navigation filter and a fault-detection filter. The sensor-fusion algorithm integrates the measurements from all N sensors and generates a single, optimal estimate of the navigation state vector.
In contrast to this, the schematic shown in FIGURE 2 is the parallel filtering approach introduced in this article. In this case, the same N sensors are divided up into M separate sensor clusters.
FIGURE 2. Parallel filtering architecture.
The measurements from the sensors in the jth cluster is processed in a sensor-fusion algorithm to generate an estimate of the state vector denoted xj and a covariance matrix Pj. Each pair (xj, Pj) is then sent to a blending filter that generates a single optimal estimate and P. The estimate is a weighted sum of the estimates from the M filters:
(1)
where Bj are blending weights that function as switches, which can be “opened” (set to zero) to isolate a parallel filter momentarily or permanently when a failed sensor is detected. The analogy with a physical switch should not be taken literally, however, because they are not “hard on-off” switches. Instead, they are matrices, which serve to change the emphasis put on a particular parallel filter. The blending weights are calculated so that the estimateis an unbiased minimum-variance estimate. In mathematical terms, this means that they minimize the trace of the final covariance P. We will give more detail on how to calculate the weights shortly, but before we do that, let us describe, at a high level, how all of this works.
Consider that one of the sensors in the th cluster fails. Theth fault detection filter will identify the fault and try to isolate it. If the fault is non-isolable, the th fault detection filter will raise an alarm. This can be done in various ways including inflation of the th filter covariance . An increasing covariance matrixleads to a decreasing value of the corresponding blending weight . For a non-isolable fault, will eventually approach zero, which effectively isolates the th cluster from the navigation solution. If the fault was just a momentary glitch, thenand are reset. In the simplest case, can be reset to a weighted sum of remaining M-1 parallel state estimates. This is then blended with all of the other parallel estimates for generating the new . This does not require setting aside buffers to store delayed measurements. Neither does it require rewinding the solution back in time when recovering from a faulted sensor scenario.
Mathematical Formulation
Providing a detailed derivation of the parallel filter is beyond the scope of this short article. Instead, we will just summarize the steps in the parallel filtering algorithm with the key formulas that are used in determining the blending weights. For simplicity, we will assume that we are working with a system with two parallel filters (M = 2 in Figure 2). How this extends to systems with more parallel filters or complex interlinking between the filters will become apparent later in the article when we present the results from a case study.
To start, let us define some notation. We assume that the two parallel filters are extended Kalman filters (EKFs) generating estimates of the state vectors x1 and x2. We will denote these estimatesand . The covariances for these estimates are denoted by P1 and P2, respectively. The output of the blending filter is an estimate of the state vector x, which is a subset of x1 and x2. In mathematical terms, this means that we can define two mapping matrices M1 and M2 whose entries are either “1” or “0” and:
(2)
The output of the blending filteris, thus, given by:
. (3)
The blending weights are computed from:
(4)
(5)
where
(6)
(7)
. (8)
The covariance ofis given by:
(9)
whereand Π is given by:
(10)
where P12 is the cross-correlation between the states of parallel filter #1 and #2. We will say more about this shortly. In the meantime, note that in Equation (9), P1 and P2 are the covariances computed by the parallel filters after the measurement update. This computation requires knowledge of K1 and K2, which are the EKF gains for parallel filters. The matrices H1 and H2 are the observation matrices for filters #1 and #2. They relate the measurements y1 and y2 of the two parallel filters to their respective state vectors as follows (refer to Figure 2):
y1 = H1x1 + v1 (11)
y2 = H2x2 + v2 (12)
where v1 and v2 are the measurement noises. Thus, the blending filter has to have knowledge of the measurement model and the gains of each parallel filter.
Finally, note that P12 is zero if the dynamic models (time update equations) for the two parallel filters are completely independent. However, if they share sensors then there will be a correlation and P12 ≠ 0. This is the case for the example we present later in this article. In this case, P12 needs to be propagated between measurement updates. This can be done with the covariance time update equation (Lyapunov equation) for the joint state vector
.
Note that the architecture depicted in Figure 2 is meant to be a high-level depiction of the idea of parallel filtering. It should not be interpreted as an actual system architecture schematic. This will become apparent in the case study we present later in this article. The system we will consider there consists of three filters of which two are run in series (cascaded so that the output of the first is the input of the second) and each, in turn, is run in parallel with the third filter.
It is important to note that the proper blending of the various filters’ outputs hinges on an accurate estimate of the individual covariances. This is particularly true when a fault has occurred. An individual filter that has detected a failed sensor must inflate its covariance to reflect its faulted state. How a filter does this is the problem of fault-detection filter design and is beyond the scope of this short article. For the work presented here, we used fault-detection filters, which monitored the EKF measurement residuals to detect sensor faults. When these filters detected a fault, they immediately inflated the faulted sensor’s output noise covariance matrix. We cannot overemphasize, therefore, the importance of having a well-designed fault-detection filter that responds in a timely and accurate manner to sensor faults.
Case Study: Small UAV Flight Control
detection/isolation scheme described above, we discuss the results of a blending filter, which was used on the University of Minnesota UAV Laboratory Goldy flight control system (FCS) shown in FIGURE 3. The Goldy FCS is used for navigation, guidance and control of small UAVs. The results presented below were obtained by post-processing flight test data.
FIGURE 3. Goldy flight control system.
The architecture of the parallel filtering scheme used is shown in FIGURE 4. There are three separate filters whose outputs are blended: a GNSS-aided inertial navigation system (INS) filter, an attitude heading reference system (AHRS) filter and an airspeed-based dead-reckoning (DR) filter. Two blending filters are used to fuse the outputs from these three filters. The first blending filter fuses the attitude estimates from a GNSS-aided INS and an AHRS. The second blending filter fuses the position solutions from the GNSS-aided INS and the airspeed-based DR system. The AHRS and the airspeed-based DR filters are a pair of filters, which are cascaded to generate an estimate of the UAV navigation state vector. Thus, in the case of GNSS-denied operations, it can provide a position, velocity and attitude estimate to the flight control system. All of the sensors and software required to run these filters are part of the Goldy FCS. Before we present results of the parallel filter’s performance, we will briefly describe these three systems below.
FIGURE 4. Goldy parallel filtering architecture. The three-axis magnetometer (Mag.) feeding the attitude heading reference system (AHRS) filter is part of the inertial measurement unit (IMU) device. The device’s accelerometer and gyro outputs feed both the GNSS-INS and AHRS filters. A pitot tube device supplies airspeed measurements to the airspeed-based dead-reckoning (DR) filter.
The GNSS-aided INS uses a consumer/automotive grade inertial measurement unit (IMU) to generate a position, velocity and attitude solution at a rate of 50 Hz. A 1-Hz measurement update from GPS is used to arrest drift errors inherent in inertial navigation systems, especially those mechanized using low cost consumer/automotive grade sensors. The GPS position updates also allow estimation of the inertial sensor biases. The state vector for this GNSS-aided INS is denoted x1 and consists of the following 15 states: latitude (Λ), longitude (λ), altitude (h), north velocity (Vn), east velocity (Ve), down velocity (Vd), roll angle (φ), pitch angle (θ), yaw angle (ψ), three gyro biases (bp, bq and br) and three accelerometer biases (bax, bay and baz).
The second and third filters are a pair of estimators connected in series. The AHRS filter generates attitude estimates, which are fed to the airspeed-based DR filter. The AHRS uses the same IMU as the GNSS-aided INS to estimate roll (φ), pitch (θ) and yaw (ψ) attitude states of the vehicle as well as the three gyro biases (bp, bq and br). This AHRS filter’s six-dimensional state vector is denoted x2. The attitude is then used to resolve airspeed measurements from the body frame of the UAV to the north-east-down coordinate frame. After adding an estimate of the local winds to this, a single integration yields a position solution. This is done at a rate of 50 Hz. A periodic 1-Hz update from GPS is used to arrest the inherent DR drift. It also allows estimation of the magnitude of the local winds. The state vector of the airspeed-DR is denoted x3 and consists of the following 11 states: latitude (Λ), longitude (λ), altitude (h), local north wind speed (Wn), local east wind speed (We), yaw angle offset (Δψ), pitch angle offset (Δθ), three airspeed-measurement biases (Ub, Vb and Wb), and altitude offset (Δh).
In the UAV flight control system, the blended states of interest are position (Λ, λ and h) and attitude (φ, θ and ψ). This implies that four mapping matrices are required for the fusion. First, matrices are needed for the attitude blending using the GNSS-aided INS (M1a) and the AHRS (M2). Then, additional matrices are needed for the position blending using the GNSS-aided INS (M1b) and the airspeed-based DR (M3). The shaping matrices are given by:
(13)
(14)
(15)
(16)
where Ij×k is a j × k identity matrix and Zj×k is a j × k matrix of zeros.
Filter Performance
Validation of the parallel filtering scheme was accomplished by post-processing data from a series of flight tests where the Goldy FCS was installed on a UAV flying around a box-shaped trajectory.
The first set of results was from a case where GPS was available from the moment the FCS is turned on until shortly after takeoff. Thus, GPS was available during initialization, take off roll and initial climb of the UAV. Then, GPS services were interrupted for a three-minute period during flight and restored shortly before the UAV landed. The GPS interruption was simulated by cutting out the 1-Hz measurement updates to the GNSS-aided INS and the AHRS/airspeed-DR system. In the background, however, there was another GNSS-aided INS that had an uninterrupted GPS service throughout the entire flight. This additional GNSS-aided INS solution is referred to as the reference solution and is used as ground-truth for assessing the performance of the parallel filtering scheme. For example, error plots shown below were generated by taking the difference between the various filtering schemes under consideration and this reference solution.
FIGURE 5 shows the errors in the attitude of all three filters during this flight test. It shows that the blended estimates of heading, pitch and roll tend to oscillate closer to zero error than either of the individual filters themselves. This is reflected in TABLE 1, where it can be noted that the root-mean-square (RMS) error of the blended solution is lower than either the GNSS-aided INS or the AHRS in each of the three attitude solutions.
FIGURE 5. Attitude errors. The gray vertical lines indicate when GPS availability was interrupted and then restored.Table 1. RMS orientation errors of different solutions (in degrees).
FIGURE 6 shows the position errors of all three systems and illustrates one of the primary advantages of the proposed architecture. FIGURE 7 and FIGURE 8 show the blending weights matrices B1 and B2 before, during, and after the GPS outage. What is shown in these figures are the diagonal elements of these matrices.
FIGURE 6. Position errors during a GPS outage.FIGURE 7. Attitude blending weights.FIGURE 8. Position blending weights.
The INS exhibits extreme drift errors after only three minutes of unaided operation. The blending algorithm detects this inaccuracy and places more weight on the slow-drifting AHRS-DR solution, as shown in Figure 8. When GPS services are restored, the GNSS-aided INS error is “reset,” and the position weights are re-established to their pre-outage levels with minimal transient responses.
We next show data from another flight test where an unplanned but fortuitous fault in the GPS sensor occurred. The cause of this fault has not been definitively determined, but potential reasons for it include loose cabling or outdated firmware. Nevertheless, this fault provided useful flight data for our architecture as no fictitious or simulated data was used. FIGURE 9 shows the GPS altitude measurements during this flight test. At t = 44 seconds a large oscillatory GPS error occurred. Similar errors were present in the GPS measurements of the velocities, latitude and longitude.
FIGURE 9. GPS sensor errors during a fault.
Thus, all filters were initialized and operated correctly for the first 44 seconds. Between 44 and 132 seconds, the GPS receiver output was in error. This time period corresponds to the taxi, takeoff and initial climb phase of the UAV’s flight. A “reference” GNSS-aided INS, which did not employ the fault detection and isolation scheme that was employed in the parallel filtering system, was running in real time for this flight test. However, the UAV was under manual control (fortunately). As shown by the gray solution in FIGURE 10, the “reference” (non-fault-tolerant) system running in the background diverged and never converged.
FIGURE 10. Attitude solution during an actual GPS sensor failure.
The dark traces in Figure 10 show the performance of the fault detection and isolation algorithm paired with the parallel filtering scheme described in this article. It is seen to be fault-tolerant and ignores the invalid measurements. Although nearly no aiding was provided until after the GPS sensor converged back to a stable state, the fault tolerant filter provided a much more accurate solution.
A bird’s eye view of the ground track of the UAV shows a similar trend. This can be seen in the position plot of FIGURE 11, which shows a roughly 60-second segment of the flight.
FIGURE 11. GPS sensor failure performance: north vs. east.
This north vs. east plot demonstrates that a non-fault-tolerant GNSS-aided INS provides an unstable position solution similar to the attitude shown in Figure 10. By contrast, the fault-tolerant system described in this article provides a smooth position estimate that ignores the “bad” GPS measurements and tracks the “good” measurements after they convergence back to the truth. Therefore, the safety of the aircraft would not have been in question, and the UAV could have completed multiple segments of fully autonomous waypoint navigation in spite of the faulty sensor measurements provided earlier.
Summary
The parallel filtering approach discussed in this article has the potential for providing a systematic way of designing multi-sensor navigation systems, which are robust to sensor faults. Unlike prior approaches, it obviates the need to maintain data buffers to store data, which can be played back in the event of a sensor fault. As noted earlier, like any engineering solution to problems, this one is a comprise between many competing requirements. As such, it has some drawbacks when compared to traditional approaches. We note two of them here as they are the focus of ongoing work. First, the computational overhead associated with this approach can be high especially if a large number of parallel filters are used. Thus, methods for streamlining the computations so that they are not computer-resource intensive will be important.
The second issue that needs further exploration is the way in which blending weights are computed. A key input to calculating the weights (as well as the “triggers” for the fault detection and isolation algorithm) are the covariances estimated by the various parallel filters. This can be problematic if the covariances used by the parallel filters do not match the true statistics. This can lead to turning off a particular filter when no faults had occurred or, worse, retaining a filter with a failed sensor in the blended solution.
This article is based, in part, on the paper “A Fault-Tolerant, Integrated Navigation System Architecture for UAVs” presented at ION ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif., January 26–28, 2015. The contents of this article reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The authors acknowledge the United States Department of Homeland Security for supporting the work reported here through the National Center for Border Security and Immigration under grant number 2008-ST-061-BS0002. However, any opinions, findings, conclusions or recommendations in this article are those of the authors and do not necessarily reflect views of the United States Department of Homeland Security.
Manufacturers
The Goldy FCS uses a Hemisphere GNSS Crescent OEM board and an Analog Devices ADIS16405 iSensor MEMS inertial measurement unit.
Trevor Layh is a M.S. candidate in the Department of Aerospace Engineering and Mechanics at the University of Minnesota in Minneapolis. He obtained his B.S. in mechanical engineering from South Dakota State University, Brookings, S.D., and his research interests include backup navigation systems to GPS-aided inertial navigation systems.
Demoz Gebre-Egziabher is an associate professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota. His research focuses on the design of multi-sensor navigation systems. He holds a Ph.D. in aeronautics and astronautics from Stanford University, Stanford, Calif.
FURTHER READING
• Authors’ Conference Paper
“A Fault-Tolerant, Integrated Navigation System Architecture for UAVs” by T. Layh and D. Gebre-Egziabher in Proceedings of ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif. January 26–28, 2015, pp. 702–712.
• Attitude Heading Reference System and Airspeed-Based Dead Reckoning Filters
Correlated-Data Fusion and Cooperative Aiding in GNSS-Stressed or Denied Environments by H. Mokhtarzadeh, Ph.D. dissertation, University of Minnesota UAV Laboratories, 2014.
“A Recovery System for SUAV Operations in GPS-Denied Environments Using Timing Advance Measurements” by T. Layh, J. Larson, J. Jackson, B. Taylor and D. Gebre-Egziabher in Proceedings of ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif. January 26–28, 2015, pp. 293–303.
• UMN UAV Research Lab and Goldy Flight Control System
“Infrastructure” website, University of Minnesota UAV Laboratories, July 2014.
• Navigation in GPS-Denied Environments
Impact and Mitigation of GPS-Unavailability on Small UAV Navigation, Guidance and Control by D. Gebre-Egziabher and B. Taylor, Technical Report 2012-2, University of Minnesota, Department of Aerospace Engineering and Mechanics, November 2012. Available through online request.
• Avionics Reliability
Introduction to Avionics Systems, 2nd Edition, by R.P.G Collinson. Published by Kluwer Academic Publishers, Boston, Mass., 2003.
Civil Avionics Systems by I. Moir and A. Seabridge. AIAA Education Series. Published by American Institute of Aeronautics and Astronautics, Reston, Va., 2003.
• Example of a Fault-Tolerant Avionics System
“Performance of Honeywell’s Inertial/GPS Hybrid (HIGH) for RNP Operations” by C. Call, M. Ibis, J. McDonald and K. Vanderwerf in Proceedings of PLANS 2006, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Coronado (San Diego), Calif., April 25–27, 2006, pp. 244–255, doi: 10.1109/PLANS.2006.1650610.
A fully autonomous, unmanned aerial vehicle (UAV)-based system for locating GPS jammers, currently under development, seeks to localize a jammer to within 30 meters in less than 15 minutes in an area comparable to that of an airport. Ultimately, the design team targets the ability to locate multiple, simultaneous jammers, and navigate in intermittent GPS and GPS-denied environments using a combination of GPS and alternate navigation aids. The system should be inexpensive and built from commercially available or open-source parts and software.
By James Spicer, Adrien Perkins, Louis Dressel, Mark James, Yu-Hsuan Chen, Sherman Lo , David S. De Lorenzo and Per Enge, Stanford University
The aviation community worries about GPS jamming. Recently, it struggled to find so-called personal privacy devices on Newark’s Liberty International Airport and traveling the nearby New Jersey Turnpike.
A number of unintentional jamming incidents took a long time to resolve. The disruption from an intentional, malicious jamming attack could be far worse. Airport authorities should be prepared to locate and shut down a coordinated attack by numerous jammers capable of disrupting the GPS service over an entire airport.
The closure of a major airport for the many hours or days it would take to locate even a couple of backpack-sized transmitters would be not only be highly disruptive in flights delayed or diverted, it would negatively impact the confidence of the flying public.
Any system in place to mitigate this threat must be inexpensive enough to be deployed at least at the nation’s major commercial airports, autonomous enough to be operable with limited training and certification, and rapid and accurate enough that a jammer can be routinely apprehended by ground-based law enforcement. It must be able to navigate successfully in GPS-denied environments using alternative position, navigation and timing (APNT), and have the range and capacity to search an airport-sized area as well as the approach corridor leading to runway touchdown.
This article describes such a system and device presently in research and development: the Jammer Acquisition with GPS Exploration & Reconnaissance (JAGER).
Vehicle Design and Operation
The JAGER UAV is a based on a commercially available, multi-rotor airframe modified to suit the mission specifications. The 1.2-meter diameter octocopter has a maximum takeoff weight of 11 kilograms (24.2 pounds), a top speed of 20 meters/second (m/s, 45 mph), and can fly unloaded for up to 30 minutes.
We have replaced the battery tray with our own carbon fiber design that allows us to carry 16 Ah of lithium polymer batteries for a maximum power draw of 4 kW. This extra capacity means that even with a 5-kilo experimental payload, the present craft can remain aloft for up to 15 minutes without recharging.
The payload plates are also custom-made from carbon fiber, and it is to these that the UAV’s experimental payloads are mounted (see FIGURES 1 and 2). One payload plate is flown at a time, and is secured on top of the airframe with a quick-release mechanism. This modularity allows for individual experiments to be mounted to their own payload plate and ground-tested before being secured to the UAV. Different experiments can be switched out rapidly for efficient use of battery capacity and flight time.
Figure 1. (A) Diagram of the payload plate showing regularly spaced mounting holes. (B) Plate with APNT experiment mounted. (C) Payload plate / experiment assembly secured atop JAGER UAV.Figure 2. Image of the vehicle showing the battery tray slung beneath the central body, the APNT experiment and payload plate secured on top, and the jammer-hunting antenna mounted at the front.
The plate itself also offers flexibility for component mounting. Regularly spaced, threaded holes across the plate mean components’ positions can be easily changed to find an optimal configuration. This can be particularly useful for minimizing interference between computers and noise-sensitive components such as antennas and magnetometers.
Software. We modified existing, open-source autopilot software to fly the mission. The craft is fully capable of completing a mission autonomously, but also can be taken over by a human pilot if necessary. A ground station also can be used to send commands to the octocopter, but is primarily used to monitor UAV location, battery life, and jammer belief state.
The autopilot software also has been adapted to communicate with various vehicle payloads. Experiments using APNT equipment, for example, pass their data to the autopilot, which will combine these signals with its own GPS data for accurate navigation in areas where the GPS signal might be intermittent or unreliable. In return, the autopilot can be used to pass data to experiments reliant on altitude, attitude, atmospheric pressure or location information.
The ground station monitors instruments’ data and status in real time. This not only allows for control of airborne experiments, but also straightforward ground testing. Synthetic autopilot data can be fed to an experiment to ensure that all systems are performing correctly before they are mounted on the vehicle for flight tests.
APNT Overview
Key to navigating in a GPS-denied environment is the use of signals from APNT networks for location determination. The proposed system should be able to navigate using any or all available APNT signals, and should weight each one according to its strength and reliability in order to formulate the most accurate estimate of both its own and the jammer’s position.
Here we describe the use of the universal access transceiver (UAT) and distance measuring equipment (DME) network for our APNT signals. The UAT signal has been implemented by the Federal Aviation Administration (FAA) in the United States as part of automatic dependent surveillance–broadcast (ADS-B), and is transmitted through a network of terrestrial ground stations.
The ADS-B network was only completed across the contiguous United States in 2014, so it is new compared to established cellphone networks. It is more comprehensive than many other terrestrial systems, so that coverage of most airports is guaranteed. While GPS reception requires an unobstructed view of the sky, UAT reception requires a direct line of sight to a transmitting tower. However, the flatness of terrain surrounding most airports as well as the UAV’s airborne vantage point ensures that UAT signals will probably be visible throughout most jammer-seeking missions.
The APNT equipment used for navigation by the JAGER UAV consists of UAT (978 MHz), DME (982 to 1213 MHz), and GPS (1575.4 MHz) antennas, a multichannel transceiver to combine the two signals, and a computer for data processing (see FIGURE 3). A dedicated lithium-ion battery powers the entire APNT payload. The current system does incorporate GPS to estimate the time offset, but future iterations of the system will derive time from sources other than GNSS so that true GPS-denied navigation is possible.
Figure 3. Schematic of the APNT configuration on board the JAGER UAV. Resulting location information is passed to the autopilot for navigation.
The UAT antenna receives multiple signals from visible ADS-B ground station transmitters. The transceiver combines these with a GPS timestamp, and the data is passed to the APNT computer for analysis. Based on knowledge of the absolute locations of the ADS-B antennas, the range of the vehicle from each antenna can be calculated, which in turn can be used to trilaterate the vehicle’s absolute position. This position is then passed to the autopilot for the UAV’s navigation, while the status of the equipment and signal strength are passed down to the ground for monitoring in real-time.
The necessity of using GPS signals as an accurate timing system is a current limitation, as navigation in GPS-denied conditions is clearly not possible while we are using GPS as a clock. As mentioned eariler, future designs will derive time from non-GNSS sources, such as chip-scale atomic clocks or the terrestrial ranging signals.
Carrying an onboard computer allows for real-time processing of the terrestrial alternative navigation signals. However, there are a few limitations to the use of these signals. First, the vertical position is difficult to calculate due to the geometry of terrestrial signals as well as the sparsity of visible station at low elevation. This is solved by using a baro-altimeter. Second, DME signals do not provide a pseudoranging function. Current work sponsored by the FAA is developing a DME pseudoranging capability. As the technology matures, we will improve the hardware and algorithm that can be integrated into future JAGER designs, resulting in lower weight and power overhead for the APNT payload.
Tracking Overview
GPS jammers do little more than emit signals in the GPS frequency range. Because the signals from GPS satellites are so weak by the time they reach the Earth, ground-based jammers do not have to be especially powerful to overwhelm GPS in their immediate vicinity. A jammer is no more than a ground-based radio-frequency source radiating within the GPS spectrum.
The JAGER system will autonomously locate the nearest beacon emitting electromagnetic signals at the target frequency: the GPS frequency in this scenario. Testing such a system is difficult due to the illegality of jamming the GPS signal within the United States. We instead test the system using a powerful Wi-Fi beacon as a proxy for the overpowering jammer. Excepting the target frequency, the procedure to locate the jammer is identical to the GPS case.
To receive the jamming signal, the front of the craft carries an antenna optimized to receive signals of the target wavelength; the current antenna has a 60° cone of maximum sensitivity. It is angled downward 30° from the horizontal, so that the craft can receive all signals from the horizon to 30° from vertical. This gives the UAV visibility over most of the space in front and underneath it. Like the other payload equipment on the vehicle, the antenna is secured with a fast-release mechanism so that it can be easily swapped out if necessary. For Wi-Fi tracking, we use a Yagi antenna with 60° beamwidth and 9 dBi gain. In upcoming trials, we will test different antenna configurations (such as dual antennas, small antenna arrays, and directional antennas augmented with omni-directional antennas) to determine benefits of these different layouts.
Signals from the antenna are passed into a module that converts the Wi-Fi data to serial, then from serial to USB. A single-board Linux computer with a quad-core processor then analyzes the signal data (see FIGURE 4). The hardware used to locate the jammer weighs 160 grams, so has negligible impact on the vehicle’s flight time or range.
Figure 4. Schematic of the tracking system on board the JAGER UAV. The resulting believed location of the target is passed to the autopilot.
To find the jammer’s location, the UAV performs a controlled yaw spin while recording the strength of the jamming signal. On the basis of the signal landscape surrounding the vehicle, the computer estimates the jammer’s location and sends a message to the autopilot instructing the craft to fly in that direction (or, more accurately, in a direction that optimally improves the ability of JAGER to find the jammer quickly). In return, the autopilot updates the tracking computer and ground station as to the vehicle’s position.
After moving a certain distance towards the jammer’s believed location, the craft repeats the spinning maneuver and starts the process again. Although rotating only the antenna might increase the speed of the operation, the energy required to carry the necessary antenna-rotation mechanisms for the duration of a flight is more than that needed to spin the entire craft.
The tracking algorithm is not as straightforward as gradient ascent or homing, and the vehicle will not always fly in the direction of greatest signal strength. The operational area is uneven, and may include buildings, towers, or airplanes, resulting in a complicated RF environment. Signals are scattered, diffracted and reflected, meaning that an algorithm that simply follows the strongest signal will not always converge on the actual jammer location.
To decide the optimal path from the vehicle’s present location to the jammer’s believed position, the tracking algorithm makes use of partially observable Markov decision processes (POMDPs). POMDPs model decision processes where the underlying state of the system (that is, the location of the jammer) is never completely known, and maintain a probability distribution over the set of all possible states.
The entire deployment area (an airport and its environs, for example) is split up into a square grid. For every possible combination of jammer and vehicle grid square locations, the signal strength and direction that would result is calculated offline prior to deployment and stored in a database on the tracking computer.
During the mission, the UAV records its own position and the sensed jamming signal’s strength and direction. The jammer location that would correspond to this result is retrieved from the database, as well as a measure of the strength of this belief state.
Once the craft has a belief as to the location of the jammer, it moves to a new location in the jammer’s believed direction before taking another measurement of signal strength. The new location and new measurement are combined, and the updated corresponding jammer location is retrieved from the database. This process is repeated until the vehicle believes itself to be right above the jammer, at which point a photograph is taken, the ground station is notified, and the hunting mission is complete.
Having found the jammer, the system can be programmed to execute a wide range of operations. These include reporting coordinates and a live image of the believed jammer location back to the ground station, hovering above and tracking the jammer if it begins to move, landing at the jammer site, or returning to base.
We calculate and store the POMDP decisions in advance of the flight. This strategy has some advantages. First, it allows for almost instantaneous decision-making. This is because the algorithm’s decisions are based solely on the vehicle’s current location and sensory observations and not on any previous states (a defining characteristic of a Markov decision process). The craft needs only to observe its current state in order to look up its next move in the database. This enables rapid tracking in flight.
A second advantage is that safety checks can be pre-programmed into the database in advance of deployment. While JAGER is programmed to move towards the grid square believed to contain the jammer, it can also be programmed to avoid or take special precautions when moving towards or in the vicinity of certain squares in the grid (also called geo-fencing). In an airport situation, for example, the vehicle would avoid moving into the square containing a control tower or ground-based antenna, or would fly at a minimum altitude over buildings and taxiways to avoid collisions.
Finally, the integration between the autopilot and the tracking software can provide other important safeguards: in the proof-of-concept system, any navigation decision taken by the software can be relayed to the ground for human verification before the UAV begins to move. This supervised mode of operation lends itself to a seamless migration path to fully autonomous operation (always overseen by a human operator).
However, one disadvantage of calculating and storing decisions in advance is the storage space needed on the vehicle. Because the result of every possible combination of vehicle and jammer locations within the grid is calculated, the size of the database grows quickly with increasing numbers of possible positions (and states). The larger the grid or the greater the required accuracy, the more space is needed to store the database. With current algorithms, the database needed to locate a jammer to within 30 meters in an area the size of an airport requires 15 gigabytes of storage space, resulting in longer lookup times during flight.
We are considering several strategies to mitigate this disadvantage, including better compression, more effective search algorithms, and uploading from a ground server only the parts of the database that correspond to the vehicle’s current operational area. Another strategy is to use an adaptive mesh that changes in resolution depending on the jammer’s belief state: at low certainty the database resolution is low, but increases in the appropriate area as the jammer’s location becomes more certain.
Another disadvantage of pre-solving the decision-making process is that the system must be reconfigured for every site in which it is deployed. The specifications of the tracking algorithm will change depending on the requirements of the operating area. The grid size, shape and absolute location must change to suit the area being protected. The resolution of the grid depends on the required accuracy of the tracking system, and restricted or prohibited locations must suit the terrain, buildings and geological features of the deployment space. For example, a lead JAGER vehicle could be adapted and tested to suit a particular airport, and then the bespoke algorithm and database uploaded to backup vehicles in that airport’s fleet.
APNT Performance
During the Joint Interagency Field Experimentation (JIFX) event at Camp Roberts, California, in November 2014, we tested the APNT system by deploying the vehicle with GPS, UAT and DME antennas simultaneously recording data. GPS receivers on the ground were used to collect reference measurements to estimate the time of transmission of the signals from the APNT sites. All signals were recorded at an altitude of 275 meters above ground level (600 meters above sea level), at four different points roughly 800 meters apart, and the data analyzed for comparison. As expected, the UAT broadcast was noisier than the GPS signal. However, it was possible to calculate a range from the UAT data that was accurate to within 16.6 meters of the GPS reference position, well within the 30 meters error bound specified in the project specification (see FIGURE 5).
Figure 5. UAT range deviates from GPS derived range-estimate by an average of only 16.6 meters throughout the duration of the test flight.
While UAV navigation using APNT was done offline in post-processing for these tests, with planned algorithm improvements and hardware acceleration the UAT signal can be used to get real-time position information nearly as accurate as that from GPS. Thus the JAGER UAV can be navigated with comparable reliability in both GPS and GPS-denied environments.
Terrestrial APNT signals will be received at a wide range of power levels. This effect is not observed with the GPS network, as the different satellite signals are broadcast from such a great distance that any differences in received signal strength are relatively small by the time they reach Earth. For terrestrial networks, signals from transmitters close to the receiver can be many times stronger than those further away, which can result in two issues: 1) interference where one signal overwhelms another, and 2) inability to process a signal if the receiver does not have adequate dynamic range to capture strong and weak signals clearly.
This problem was observed in our tests, as we were receiving two signals: one 13.7 kilometers (DME) and the other 43.5 kilometers (ADS-B UAT) from our test site. Calculating accurate ranging estimates from the two required determining a gain setting that had dynamic range adequate for receiving both signals clearly.
Vehicle Performance
During experimental testing, the vehicle itself also underwent rigorous assessment of its performance under different conditions. Due to the delicate and often expensive nature of the payloads and experiments made possible by the JAGER platform, it is essential that the vehicle perform as expected, and that there are multiple procedures in place to protect the payloads in case of vehicle failure.
Because the open-source autopilot had never been used with such a large vehicle, we first ground-tested the craft’s flight control and stability. The vehicle was tethered and constrained to move in only one axis, and ropes were used to control its roll. While altering autopilot variables controlling roll and pitch feedback loops, we measured the vehicle’s response to impulsive disturbances and the time taken for it to right itself when upset. In this way we could tune the control gains and verify that the vehicle would be exceptionally stable during flight in even the most challenging atmospheric conditions. While we preferred to fly in the early morning hours to exploit clear air and lower winds, we did perform tests with momentary gusts of up to 7 m/s during envelope expansion flights.
We tested the vehicle with two accelerometers on board to measure how the rotors’ vibrations affected the rest of the craft. One accelerometer was attached to the airframe itself, while the other was secured to the payload plate. A comparison of the acceleration data recorded by the two instruments revealed that the payload plate experienced significantly less vibration than the airframe during flight, and both measurements remained well within the tolerances advised by the airframe manufacturer.
Two crucial flight modes also were tested before payloads were flown on the vehicle. Both altitude-control mode and position-control mode were tested to ensure that they could precisely constrain respectively the vehicle’s altitude and absolute position in a range of atmospheric conditions. Results showed that in altitude control mode, the vehicle’s z-coordinate was held constant to within ± 0.5 meters. In position control mode, its x- and y-coordinates remained within ± 1.0 meters (or a single vehicle length).
The success of the JAGER tracking mission also depends on accurate position measurements from the UAV. Operators must be confident in the vehicle’s position, so that ground forces can easily apprehend the located jammer, and also so that there is confidence in the success of safety protocols including geo-fencing, no-fly zones and minimum flight altitudes.
In addition to the geo-fencing and flight precautions taken by the tracking algorithm, the JAGER UAV has several other safety procedures executed automatically by the autopilot. A non-catastrophic error in the flight systems or payload is transmitted to the ground station for human troubleshooting, and commands can be sent to the vehicle as to how to proceed.
Finally, should we continue operations and allow its batteries to get sufficiently low, the vehicle will automatically return to launch site for landing and battery replacement. A catastrophic failure such as the loss of a motor will result in an immediate controlled landing. The craft can also be commanded from the ground station to land or return to launch, and can be taken over by a human pilot at any time.
Other tests verified that the vehicle has the range and endurance to be successful when deployed in an airport setting. When fully loaded with APNT and tracking payloads, the UAV exhibited a top speed of 10 m/s, enough to cover the length of an A380-capable runway in less than 5 minutes. A 20-minute flight endurance means that even including hovering during jamming signal observations by the tracking antenna, the JAGER system can hunt easily and effectively throughout an airport-sized area. Furthermore, we continue to explore techniques to improve dash capability, including reducing the weight of the APNT payload, and we anticipate describing results of these efforts in future reports.
Electromagnetic Interference
Because of the payload tray’s small area (0.5 m2), electromagnetic interference (EMI) between APNT components was a significant issue during testing. The GPS and UAT receivers are extremely sensitive to interference from other sources emitting in the frequency ranges to which they are tuned. The APNT computer, by contrast, is composed of various processors, clocks, drives and power boards that emit powerful electromagnetic noise at a wide range of frequencies as a byproduct of their normal operation.
The size and mass of the APNT computer board meant that it had to be mounted in the center of the payload tray to avoid unbalancing the UAV. That left a maximum 7 centimeters of space around the computer on which to mount the two antennas (see FIGURE 6). With no shielding, the EMI from the computer proved powerful enough to completely overwhelm the GPS, UAT and DME network signals, making navigation and position estimation using any network impossible.
Figure 6. Diagram showing the APNT experimental payload, and the proximity of the EMI-radiating CPU to numerous antennas.
The EMI problem was solved in three ways. Masts were used to raise the receiving antennas to a height of 19 centimeters above the payload tray, the maximum height at which a mast collapse wouldn’t cause catastrophic rotor and vehicle failure.
The antennas also were moved around the edge of the payload tray so as to be furthest from the system components radiating at their particular frequency. Two devices that proved particularly problematic were the solid-state hard drive in the CPU and the telemetry radio antenna, which radiated EMI that interfered with the GPS and UAT frequencies respectively. This was solved by moving the telemetry antenna to the underside of the craft, and the GPS antenna to the far side of the payload plate from the hard drive. The flexible design of the payload plate described earlier ensured that the relocation and testing of components was a straightforward process.
Shielding, however, proved to be the most important factor in eliminating EMI. Custom-made copper shields were added to the two masts to shield the antennas from the computer below them while still allowing an unobstructed view of the sky (see PHOTO). We tested numerous shielding iterations, including wire meshes and aluminum and lead foils; however; all were ineffective due to the strength and wide range of EMI wavelengths emitted. Finally, the computer itself was covered in a 2-millimeter layer of copper and 1-millimeter steel sheet. This combination struck the best balance between effectiveness and weight: aluminum was light but proved ineffective at shielding, while lead was very effective at EMI shielding but was too heavy for the UAV to carry.
The APNT payload prior to installation of the DME antenna. The copper shielding on the CPU and antennas can be clearly seen.
Conclusions
The development of the JAGER system contributes to U.S. preparation for a GPS jamming attack on civil aviation. While the first iteration described here is a significant improvement on previous jammer-hunting systems, future iterations of the JAGER UAV will be able to successfully navigate in a GPS-denied environment using alternative navigation signals including UAT and DME, and broadcast an accurate estimate of their position down to the ground.
The use of an octocopter flight system gives speed, maneuverability and sensory perception that far exceed any ground-based tracking effort. A fully loaded top speed of 10 m/s and almost instantaneous direction changes allow for efficient hunting over an airport-sized area and the location of a GPS jammer to within 30 meters, within a 20-minute flight endurance.
As the JAGER system can be entirely assembled from commercially available or open-source components and operates entirely autonomously, the system provides a low-cost, readily obtainable solution to the problem of GPS jamming. This means that it can be deployed quickly and is operable without extensive prior training.
The integration of autopilot, APNT navigation and tracking systems also allows for comprehensive monitoring and control of the UAV from the ground. Telemetry and data links to the ground station provide real-time updates as to the craft’s position, the jammer’s believed location and the status of all systems and instruments running on the vehicle. Safety protocols implemented in the software ensure that there is no risk of collision with site buildings, vehicles or personnel.
JAGER’s modular design gives operators extensive flexibility in situations that are capable of being successfully resolved by the system. The switching of equipment and software to allow the UAV to use GPS navigation to hunt a UAT or DME jammer, for example, could be effected in a matter of seconds.
The JAGER system also provides a reliable test platform for any experiment that requires airborne operation. The exceptional stability of the airframe combined with extended flight time, high top speeds and pinpoint positioning lends the system to a wide variety of applications beyond jammer tracking, including network monitoring, atmospheric experiments and biological research.
Manufacturers
The JAGER UAV airframe is a S1000 octocopter by DJI Innovations, Shenzhen, China; the flight batteries are a 8000 mAh model by Hextronik, Dongguan, China; the autopilot hardware and GPS antenna is a Pixhawk by 3D Robotics, Inc., San Diego, California; the autopilot software is based on PX4 by Pixhawk.org. The JAGER navigation GPS is made by u-blox, and the receiver for the APNT clock is made by Trimble. The UAT hardware includes an ASR-2300 multichannel transceiver by Loctronix Corporation, Woodinville, Washington; the tracking hardware comprises a 2.4 GHz Yagi antenna from L-com, North Andover, Massachusetts; an RN-XV Wi-Fi module by Roving Networks, Chandler, Arizona; and an Odroid-U3 computer by Hardkernel Co., Gyeonggi, South Korea.
James Spicer is pursuing concurrent bachelor’s and master’s degrees in aeronautics and astronautics at Stanford University.
Adrien Perkins is a Ph.D. candidate in aeronautics and astronautics at the Stanford University GPS Laboratory. He received his undergraduate degree in mechanical aerospace engineering at Rutgers University.
Louis Dressel is a graduate student at Stanford University. He received his undergraduate degree in aerospace engineering from Georgia Tech, with a minor in computer science.
Mark James is a master’s student in aeronautics and astronautics at Stanford University.
Yu-Hsuan Chen is a research associate at the Stanford GPS Laboratory. He received his Ph.D. in electrical engineering from National Cheng Kung University, Taiwan.
Sherman Lo is a senior research engineer at the Stanford GPS Laboratory.
David S. De Lorenzo is a principal research engineer at Polaris Wireless and a consulting research associate to the Stanford GPS Laboratory.
Per Enge is a professor of aeronautics and astronautics at Stanford University, where he is the Vance D. and Arlene C. Coffman Professor in the School of Engineering. He directs the Stanford GPS Laboratory.
At Unmanned Systems 2015, held May 4-7 in Atlanta, Geomatics USA’s Ahmed Mohamed showcases an unmanned aerial system (UAS) that used the company’s GPS technology to takeoff and land UAS quadcopters from its structure. Geomatics USA also offers its G-AT: Active Target for surveying and mapping.
NovAtel’s Peter Soar shares on the company’s GAJT (“Gadget”), a single unit GPS anti-jam antenna for use in UAVs (unmanned aerial vehicles). GAJT nullifies jammers, ensuring satellite signals necessary to compute position and time are always available.
GAJT may integrate into unmanned vehicle platforms or can be retrofitted with GPS receivers and vehicle navigation systems on military fleets.
Septentrio has launched the AsteRx-m UAS, an RTK-accurate GNSS receiver solution specially designed for the drone market. The AsteRx-m UAS provides high-accuracy GNSS positioning with low power consumption, according to Septentrio.
The launch of the AsteRx-m UAS board is complemented by the release of GeoTagZ software suite. The GeoTagZ suite works with the UAS camera and image-processing solution to provide centimeter-accurate position tagging of images without the need for a real-time data link.
The AsteRx-m UAS will be on display at booth #635 during AUVSI’s Unmanned Systems 2015, held May 4-7 at the Georgia World Congress Center in Atlanta.
Despite being Septentrio’s smallest receiver, the AsteRx-m UAS provides consistent, robust and accurate positioning from to Septentrio’s in house GNSS+ algorithm technology. The receiver delivers cm-level accuracy at less than 600 mW with GPS and less than 700 mW with GLONASS. LOCK+ technology guarantees tracking under heavy usage and IONO+ guarantees no interference in challenging ionospheric conditions, Septentrio said.
Integration into Any UAS. One of the key characteristics of AsteRx-m UAS and GeoTagZ is the seamless integration into any UAS. AsteRx-m UAS features standard connection functionality that directly connects to a UAS autopilot, such as Pixhawk and Ardupilot. The power comes directly from a number of power sources, including micro USB, a 9-30V external power supply or the vehicle power bus. GeoTagZ is available as a library of software to integrate into an UAS image-processing tool chain.
“We want to make UAS-based data collection and processing extremely simple. AsteRx-m UAS and GeoTagZ do just that,” said Jan Leyssens, commercial product manager at Septentrio. “The GNSS board connects seamlessly to standard hardware and cameras used on a drone. Together with our software, we provide a data collection solution that provides cm-level accuracy without the need for ground control points or real-time data links, and that integrates effortlessly with an existing UAS image processing software solutions.”
Phase One Industrial, a manufacturer of medium format aerial photography equipment and software solutions, has released Phase One iX Capture 2.0, a control, capture and RAW conversion application designed specifically for aerial photography.
Features include:
Support for up to six cameras. iX Capture 2.0 can support full oblique/nadir arrays with multiple Phase One aerial cameras or dual-camera arrays, such as RGB/NIR or arrays to capture wide swaths.
Auto Exposure mode. After a user selects a priority with specific ranges set for each parameter, the camera can evaluate the current image and adjust the ISO, aperture and shutter speed for subsequent captures. Auto exposure mode helps operators avoid post flight adjustments in exposure when light conditions change.
Offline processing of files and complete folders. iX Capture can now process images post flight, enabling users to process files previously captured or even process the same files, but with different settings applied. With a choice of three offline processing recipes, individual images or folders can be processed individually or simultaneously.
AirMap — a free, comprehensive digital map — allows unmanned aircraft system (UAS) operators to visualize the airspace around them, including areas where they may not be permitted to fly.
AirMap removes barriers to compliance of complex airspace rules by providing the low altitude airspace information that unmanned aircraft operators need. AirMap was cofounded by aviation expert and entrepreneur Ben Marcus and Gregory McNeal, a legal scholar on drones, public policy and air rights.
AirMap integrates multiple sources of reliable data and gives UAS operators an easy-to-use, yet detailed, solution providing a single view of the restricted areas around an area of operations, its makers said. The beta version of the site is now live in the U.S. and will launch soon internationally, enabling UAS operators to immediately start benefiting from the free service. AirMap also features a feedback function that will allow beta testers to request additional features.
AirMap is a fully digital map that shows only the airspace rules that impact UAS operators. By focusing on airspace information from ground level up to 500 feet, AirMap strips away the clutter of higher altitude airspace labels found on charts that were created for manned aviation, its makers said.
When using AirMap, an operator can customize their display based on the type of operation they are involved in. Operators can select layers depicting the following:
Recreational use, which will display the airspace areas around airports which are limited by community-based guidelines;
“Blanket COA” rules applicable to holders of FAA Section 333 exemptions for commercial UAS operations; and
Controlled airspace (Class B, C, D, and E) at 500 feet and below, allowing UAS operators to voluntarily comply with the airspace rules proposed in the FAA’s recent Notice of Proposed Rulemaking on the Operation and Certification of Small Unmanned Aircraft Systems.
“As UAS use continues to expand, the airspace in which operators are flying is also growing more complex. With this in mind, we’ve launched AirMap, which will serve as a resource for drone operators to immediately fly safely and in compliance with legal requirements. We want to make safe flying easy,” Marcus said.
Marcus and McNeal teamed to launch AirMap after they realized that operators needed a tool that would let them understand the complexities of restricted airspace for unmanned aircraft operations. Marcus, who co-founded aircraft brokerage firm jetAVIVA, will lead development and business growth functions. McNeal will apply his expertise and research in local regulatory environments to help AirMap reach and educate users throughout the country. In addition to his role with AirMap, McNeal is an associate professor of law and public policy at Pepperdine University and a Forbes contributor.
“As a drone operator I found it hard to know what the airspace rules were in the places where I wanted to fly. There were no accurate visuals or reliable electronic tools that could tell me and other operators where we can and cannot fly. AirMap solves this problem and helps to educate operators about this complex regulatory environment,” said McNeal. “The demand for AirMap is clear, as it is the most thorough resource for drone operators to ensure safe, legal and hassle-free flight.”
AirMap’s advisory board includes Steve Crocker, chairman of the ICANN; Stuart Banner, UCLA law professor and author of Who Owns the Sky?; Tom McInerney, former scientist at Apple; and Mike Mothner, founder and CEO of WPromote.
In February 2015, AirMap launched its first service, NoFlyZone.org, which accepts registrations from property owners who prefer UAS not overfly their land. These parcels are displayed in AirMap to help operators avoid sensitive areas, and minimize the hassles associated with disputes about where unmanned aircraft should be operating. AirMap also displays hospitals, schools and helipads and will be adding other sensitive sites in the future.
Check back throughout the week for event updates, including news, photos, videos, tweets and more.
Check back throughout the week for event updates, including news, photos, videos, tweets and more.
